Despite the vast evidence on the environmental influence in neurodegenerative diseases, those considering a geospatial approach are scarce. We conducted a systematic review to identify studies concerning environmental atmospheric risk factors for neurodegenerative diseases that have used geospatial analysis/tools. PubMed, Web of Science, and Scopus were searched for all scientific studies that included a neurodegenerative disease, an environmental atmospheric factor, and a geographical analysis. Of the 34 included papers, approximately 60% were related to multiple sclerosis (MS), hence being the most studied neurodegenerative disease in the context of this study. Sun exposure (n = 13) followed by the most common exhaustion gases (n = 10 for nitrogen dioxide (NO2) and n = 5 for carbon monoxide (CO)) were the most studied atmospheric factors. Only one study used a geospatial interpolation model, although 13 studies used remote sensing data to compute atmospheric factors. In 20% of papers, we found an inverse correlation between sun exposure and multiple sclerosis. No consensus was reached in the analysis of nitrogen dioxide and Parkinson’s disease, but it was related to dementia and amyotrophic lateral sclerosis. This systematic review (number CRD42020196188 in PROSPERO’s database) provides an insight into the available evidence regarding the geospatial influence of environmental factors on neurodegenerative diseases.

Risk mapping is a crucial part of spatial planning, as it optimizes the allocation of resources in its management. It is, therefore, of great interest to build tools that enhance its production. This work focuses on the implementation of a susceptibility model for different types of spatially distributed risk in a geographic information systems (GIS) Python plugin. As an example, the susceptibility model was applied to study the occurrence of wildfires in the municipality of Vila Nova de Foz Côa, Portugal. The plugin was developed to simplify the production and evaluation of susceptibility maps regarding the available geographical information. Regarding our case study, the data used corresponds to three training areas, ten years of burned areas and nine environmental variables. The model is applied to different combinations of these factors. The validation, performed with receiver operating characteristic (ROC) curves, resulted in an area under the curve (AUC) of 74% for a fire susceptibility model, calculated with the same environmental factors used in official Portuguese cartography (land use and slope) and with the optimal training area, years of information on burned area and level of land use classification. After experimenting with four variable combinations, a maximum AUC of 77% was achieved. This study confirms the suitability of the variables chosen for the production of official fire susceptibility models but leaves out the comparison between the official methodology and the methodology proposed in this work.